Previous methods used random hiding and iterative removal to enforce action completeness. Singh et al. in Hide-and-seek [1]} force the model to see different parts of the video by randomly masking different regions of the videos in each training epoch. However, randomly hiding frames does not always guarantee the discovery of new parts and also disrupts the training process. Zhong et al. in Step-by-step erasion [2]} trained a series of classifiers iteratively to find complementary parts, by erasing the predictions of predecessor classifiers from input videos. The major draw-back with this approach is the extra time cost and computational expense to train multiple classifiers. Zeng et al. [3]} propose an iterative-winners-out strategy that selects the most discriminative action instances in each training iteration and hide them in the next iteration. Liu et al. in CMCS [4]} proposed to enforce multiple branches in parallel to discover complementary pieces of an action. Each branch generates a different class activation map (def REF ). A diversity loss (introduced in [5]}) is imposed on class activation maps, which computes cosine similarities between pairs of branches and all action categories. Minimizing the diversity loss, encourages the branches to produce activations on different action parts.
